Robust segmentation and object classification in natural and medical
images.

Ph.D. Thesis Lin Yang

Abstract

Image segmentation and object classification are two fundamental tasks
in computer
vision. In this thesis, a novel segmentation algorithm based on
deformable model and
robust estimation is introduced to produce reliable segmentation
results. The algorithm
is extended to handle touching object and partially occluded image
segmentation. A
multiple class segmentation algorithm is described to achieve
multi-class "object cut".
The accurate results are achieved using the appearance and bag of
keypoints models
integrated over mean-shift patches. An affine invariant descriptor is
proposed to model
the spatial configuration of the keypoints. Besides working with 2D
image segmentation
problem, a robust, fast and accurate segmentation algorithm is
illustrated for processing
4D volumetric data. One-step forward prediction is applied to generate
the motion
prior based on motion modes learning. Two collaborative trackers are
introduced to
achieve both temporal consistency and failure recovery. Multi-class
classification algorithms
using a gentle boosting is used to classify three types of breast
cancer. The
algorithm is Grid-enabled and launched on the IBM World Community
Grid. We will
introduce a fast and robust image registration algorithm for both 2D
and 3D images.
The algorithm starts from an automatic detection of the landmarks
followed by a coarse
to fine estimation of the nonlinear mapping. The parallelization of
the algorithm on
the IBM Cell Broadband Engine (IBM Cell/B.E.) will also be explained
in details.